AI: Anomaly detection
Spot unusual production behavior before the shift ends.
A machine-learning model learns how a job usually runs from its earlier runs, then flags the stretch that falls outside that pattern, with start and end times you can review.
What it does
A flagged stretch, not a wall of charts.
Most of a shift looks normal. Anomaly detection points at the part that does not, so you start the investigation where it matters.
- Learns the usual cycle-time range for each job from earlier runs of that job.
- Flags a stretch outside that range, with a start and an end time for review.
- Shows the flagged period against the rest of the shift, so context is one glance away.
- Lets you check what else happened at that time, such as stops, material or crew changes.
Fig. One shift of cycle times, one flagged stretch
How it works
How it works, in three steps.
It learns the job
Earlier runs of the same job set the usual range for its cycle time.
It watches the run
Current production is compared with that usual behavior as the shift goes on.
You review the flag
A flagged period with start and end times is ready for you to investigate.
Find the slow stretch in your own jobs.
Bring a job you run often and we will show where its usual range sits and what would be flagged.